Abstract: The fall of labor’s share of GDP in the United States and many other countries in recent decades is well documented but its causes remain uncertain. Existing empirical assessments of trends in labor’s share typically have relied on industry or macro data, obscuring heterogeneity among firms. In this paper, we analyze micro panel data from the U.S. Economic Census since 1982 and international sources and document empirical patterns to assess a new interpretation of the fall in the labor share based on the rise of “superstar firms.” If globalization or technological changes advantage the most productive firms in each industry, product market concentration will rise as industries become increasingly dominated by superstar firms with high profits and a low share of labor in firm value-added and sales. As the importance of superstar firms increases, the aggregate labor share will tend to fall. Our hypothesis offers several testable predictions: industry sales will increasingly concentrate in a small number of firms; industries where concentration rises most will have the largest declines in the labor share; the fall in the labor share will be driven largely by between-firm reallocation rather than (primarily) a fall in the unweighted mean labor share within firms; the between-firm reallocation component of the fall in the labor share will be greatest in the sectors with the largest increases in market concentration; and finally, such patterns will be observed not only in U.S. firms, but also internationally. We find support for all of these predictions.

In the last few years, inequality has been at the center of many political and academic debates. It turns out that, although less mentioned in these debates, the rapid growth of some developing countries in the last decades has actually decreased global inequality. But then, why is there a big debate about inequality? The issue is that, on the other hand, inequality in developed countries has been increasing over time. From the perspective of the functional distribution of income between labor and capital, one of the indicators of this increase in inequality is that the labor’s share of GDP has been falling in the United States and other countries in recent decades. These forces have generated winners and losers. As economist Branko Milanovic points out with his famous “elephant chart,” the middle class of the world and the very rich of the world are the two groups whose incomes have increased more rapidly. In contrast, it can be easily seen that there are large groups of people uncomfortable with increased inequality. Moreover, the factors assumed to be causing inequality have taken a vital role in political debates and recent elections.

“Elephant Chart”: Lakner & Milanovic (2016)

In this context, it is extremely important to understand what is driving these changes in inequality. There are different approaches to understand the increase in inequality in developed countries. The two main perspectives point to the importance of top incomes and changes in the tax system (e.g. Piketty and Saez, 2014), on one hand, and to changes in the labor market, mainly related to the incorporation of technological change that is more favorable to skilled workers (e.g. Autor, 2014), on the other. More recent approaches have begun to more directly incorporate the role of firms. For example, a growing literature estimates models to separate the firm’s and employee’s contributions to wage differences via double fixed-effects models, with many studies finding that firm wage effects account for approximately 20% of the overall variance of wages and have had an increasingly important role over time (e.g. Card et al., 2016). However, while we can all see that “superstar firms” like Apple, Microsoft, Google or many others in different sectors of the economy are growing very quickly, we still do not know what their effect of inequality is.

Do these “superstar firms” increase inequality because they are responsible for the decrease in labor’s share? The paper by Autor, Dorn, Katz, Patterson and Van Reenen addresses exactly this issue. If we are interested in understanding the role of firms in the increase in inequality, it is particularly important to answer the question of whether the decrease in labor’s share of income can be explained by technological changes occurring within firms, or if it is better explained by a rise of “superstar” firms, which tend to use new technologies and are more capital-intensive. The main argument of the authors is that markets have changed in such a way that firms with superior quality, lower costs, or greater innovation get disproportionately high rewards relative to previous periods. Since these “superstar firms” have higher profit levels, they also tend to have a lower share of labor in sales and value-added. Therefore, as these firms gain market share across a wide range of sectors, the aggregate share of labor falls. In this way, “superstar firms” are one of the drivers of the decrease in labor’s share (in favor of capital’s share) of value added.

Before they start developing the evidence for this argument, the authors clearly document the fall in labor’s share of GDP in the United States and other developed countries. After that, they formalize their main argument in a model of “superstar firms,” in order to derive the set of predictions that will be taken to the data. With this model in hand, the authors use several sources of information (U.S. Economic Census, KLEMS, UN Comtrade Database, and others) to run a series of regressions and decompositions to analyze the testable predictions of the model. First, the authors find that sales concentration levels have risen in most sectors. Second, they show that the larger decreases in labor’s share are observed in industries where concentration has increased the most. Third, by comparing the weighted and unweighted mean of labor’s share, the authors conclude that the fall in labor’s share has an important component of reallocation between (and not within) firms. Furthermore, they find that the between-firm reallocation of labor’s share is greatest in the sectors that are concentrating the most. Finally, these patterns are not only present in the US but also in many European countries.

Overall, all of these findings are consistent with the idea of a rise of “superstar firms” that have lower labor’s share, and which have gained more importance by concentrating large shares of sales in different sectors of the economy. It should be noted, however, that the authors do not provide a clean causal identification of the superstar firm model. The empirical exercises are done carefully and controlling for the factors that can more clearly affect the tested relationships. The use of fixed effects and trends by industry allow the authors to obtain identification exclusively from the acceleration or deceleration of labor’s shares and concentration conditional on these controlled trends. Thus, any potential threat to this identification strategy would have to come from other factors not captured by these trends or fixed effects and which are correlated with industry concentration and inequality.

This paper makes a major contribution by pointing out the role of “superstar firms” in explaining increasing inequality and opens some avenues for future research in a direction that had not been typically considered in the literature. In this sense, a particularly interesting direction would be to use the matched employer-employee databases with census data on sales to test if industry concentration has impacts on the firm component of wages and the within and between firm decomposition in each sector.

Finally, the paper addresses the question of what is the driver of the growth of these “superstar firms.” The main debate here is whether the rise of these “superstar firms” and industry concentration are associated with competitive forces, or if they are a signal of an economy with competition problems. Increased concentration can be a result of technological changes: some sectors could be introducing technologies that have a “winner takes all” aspect. An alternative, more worrisome story is that leading firms are less exposed to competition because they can create barriers to entry or have more lobbying power. The authors provide evidence that is somewhat comforting about this point. They show that concentration is greater in industries experiencing faster technical change, approximated either by patent activity or by total factor productivity growth. However, this evidence is still subject to debate. It could be the case that these originally innovative firms are now using their market power to generate barriers to entry. This can be even more important in some technology sectors where network effects generate an important advantage to the innovators. I think this discussion is actually one of the main directions where this stream of research can be expanded and complemented in the future. In this sense, for example, sector-specific partial equilibrium models could allow formalizing the product and labor markets under innovation dynamics, and such models could be estimated using data for specific industries and structural econometrics estimation techniques.

To sum up, I think that this paper makes a major contribution by pointing out the effect of “superstar firms” on the decrease of labor’s share of GDP, and therefore increased inequality in developed countries. Additionally, this paper opens several avenues for future work in order to generate more evidence consistent with the “superstar firms” model and, critically, to understand its causes and consequences at the individual micro level, especially using matched individual and firm level databases and sector-specific analysis. To understand the relationship between firms and inequality is a key task in a world of “superstar firms,” and these are key inputs for the discussion of, for example, the roles of tax policies, labor market institutions and their relationship with the increasing heterogeneity of firms.

by Marcel Timmer (University of Groningen), Gaaitzen de Vries (University of Groningen), Klaas de Vries (The Conference Board, Brussels)

AbstractThis paper introduces the updated and extended Groningen Growth and Development Centre (GGDC) 10-Sector database. The database includes annual time series of value added and persons employed for ten broad sectors of the economy from 1950 onwards. It now includes eleven countries in Asia (China has been added compared to the previous release), nine in Latin America and eleven in Sub-Saharan Africa. We use the GGDC 10- Sector database to document patterns of structural change in developing countries. We find that the expansion of manufacturing activities during the early post World War II period was related to a growth-enhancing reallocation of resources in most countries in Asia, Africa and Latin America. This process of structural change stalled in many African and Latin American countries during the mid-1970s and 1980s. When growth rebounded in the 1990s, workers mainly relocated to market services industries, such as retail trade and distribution. Though such services have higher productivity than much of agriculture, they are not technologically dynamic and have been falling behind the world frontier.

As economies evolve and develop tremendous changes in the composition of goods and services take place. For instance, by start of World War II, one in three workers in the United States were employed in manufacturing and agriculture. A steady shift towards the service sectors since then, means that today manufacturing and agriculture only employ approximately one in eight workers. These structural changes imply the reallocation of resources and particularly labor across sectors with different productivity levels. The rate and intensity of these process has important impact on economic growth. Structural changes, therefore, have important implications for economies mainly because of three factors:

a) technological changes occur at different paces for different goods,

b) there are different patterns of demand for different goods, and

c) relative prices in the world economy do not fully reflect relative marginal productivities and marginal utilities among goods.

Industrialised nations have, generally speaking, closely followed the United States in increasing the weight of the service sector since the 1980s (if not before). It is also widely known that during the same period, recently industrialised nations such as Brazil, Mexico China, Korea or other Asian Tigers expanded employment in their domestic manufacturing sector at the same time as their GDP was increasing. But what happened with the rest of the world? The short answer is that it is remarkable how little we know about the process in the rest of the world.

In the paper distributed by NEP-HIS 2014-09-25, Timmer, Vries and Vries describe similarities and differences in the patterns of structural change across developing countries in Asia, Africa, and Latin America since the 1950s. In order to do that, Timmer and colleagues created, updated and (more than once) expanded the Groningen Growth and Development Centre (GGDC) Sector database. This database includes data from 1950 onwards on value added and persons employed for ten broad sectors of the economy for a group of countries. In its current version, the database includes eleven Asian countries (with the good news that China is now included!), nine Latin American countries, and eleven from Sub-Saharan Africa.

There are some important stylized facts that can be learned from the paper. First, since the 1950s workers relocated from agriculture into the manufacturing and to a lesser extent the (formal and informal) services sectors. Second, employment in manufacturing grew in the 1960s and early 1970s in the three continents. These changes responded to policies through which individual countries pursued to promote industry development. Along the same lines, an result from the study by Timmer and colleagues is that there has been a clear decline of the manufacturing employment share in Africa and Latin America since the mid 1970s while production and employment increasingly originate from services activities. In 2010, only 7 percent of the African and 12 percent of the Latin American workforce was employed in manufacturing. These figures contrast with what happened in Asia, where the share of manufacturing in value-added was on average 20 percent of GDP for the same year.

According to the productivity measures by Trimmer et al., the gaps for developing countries are still huge and increasing for most countries. On one hand, the authors find that labor productivity in agriculture is much lower compared to services and even lower in relation to manufacturing. In 2010, for example, the agricultural value added share in Africa was 22 percent, while the employment share was 51 percent. This suggests agricultural labor productivity is about half of that of the average in the economy. In contrast, the services value added share was 50 percent while the employment share was 37 percent, and the shares for manufacturing are 10% and 7% respectively. On the other hand, productivity levels in manufacturing and market services have been falling behind the technology frontier (US in this paper) in Latin America and Africa, and they have been increasing (at a lower rate than I would expect, though) in Asia.

a) the change in productivity of the sector holding the share of employment fixed (within-effect),

b) the change of employment in sectors with different productivity holding the productivity fixed (static-effect), and

c) the effects of the interaction between the changes in sector productivity and employment share per sector (dynamic effect).

Their results suggest that the within-effect as well as the static reallocation effect are both positive. However, the authors find that the dynamic effect is substantially negative in Africa and Latin America suggesting the reallocation of employment to sectors (services) where the productivity increase is lower. In other words, this fact suggests that the marginal productivity of additional workers in these expanding sectors was below the productivity of existing activities.

The paper has two main contributions. First, it is hard to stress enough how valuable the contribution of these authors is of constructing this new database. This task is not always valued at its worth. Creating a new database from different sources takes a large amount of work in order to achieve the consistency of concepts and definitions used in various primary data sources. Thanks to the authors, these data and documentation are now freely and publicly available online and it encourages us to continue the study of these issues. Second, the authors focus on the comparison of the productivity among these developing countries with the productivity of the technological leaders. This is the main point in this literature given that we still observe dynamic losses of relative productivity in many countries. The main challenge in order to make productivity comparisons is how to convert real value added into common currency units. To do this, the authors use this database and combine it with previous work or their own (mainly Inklaar and Timmer, 2013) to construct sector specific purchase power parity (PPP) prices. In their comparisons, they use United States as the frontier country and measure labor productivity relative to the frontier using the sector-specific PPPs.

The bottom line of the paper is that most of these developing countries have failed to generate dynamic increases in relative productivity since they reallocated workers into the sectors where productivity grows at a lower rate. Thus, the main challenges are to reallocate excess agricultural workers if they exist, and to increase the productivity in the manufacturing and services sectors. With the agricultural and (sometimes) manufacturing sectors shrinking in their employment share, the relative dynamic productivity performance of the sectors where these workers are going to locate is the crucial part of the process of convergence. The decomposition of the economies in ten sectors provides a necessary step to understand the process of structural change and its effects on productivity. However, the change in the composition of what a country produces is a result of changes at the firm level in particular markets. This stresses the need for more studies at the firm level on the determinants of the productivity relative to the frontier by sector. This is even more important in the services sector where the evidence seems to suggest the existence of a duality, where some services have a high productivity level and others are informal activities with very low productivity that just hide unemployment.

In sum, this paper adds to other excellent previous work from the same authors and gives us the big picture of structural change over the last 60 years for a larger set of developing countries. In addition, the authors have made available a new database that, combined with other data sources, can help to answer important development questions. As usual, we have made progress but still more work is needed to understand the key topic of structural change. This knowledge is necessary to implement policies that boost the productivity of firms in developing countries and, therefore, to improve the standard of living of their populations.

Disease and Development: A Reply to Bloom, Canning, and Fink

By Daron Acemoglu and Simon Johnson (both MIT)

Bloom, Canning, and Fink (2014) argue that the results in Acemoglu and Johnson (2006, 2007) are not robust because initial level of life expectancy (in 1940) should be included in our regressions of changes in GDP per capita on changes in life expectancy. We assess their claims controlling for potential lagged effects of initial life expectancy using data from 1900, employing a nonlinear estimator suggested by their framework, and using information from microeconomic estimates on the effects of improving health. There is no evidence for a positive effect of life expectancy on GDP per capita in this important historical episode.

“The game of science is, in principle, without end. He who decides one day that scientific statements do not call for any further test, and that they can be regarded as finally verified, retires from the game.”

The Logic of Scientific Discovery, Karl Popper, 1934.

Bill Gates’s Infographic.

Not a long time ago, on April 25, Bill Gates posted an infographic on his blog revealing which is the world’s deadliest animal. Sharks, bugs, snakes and many very scary animals are not even close. The mosquito has the first place by far. They carry terrible diseases, including malaria, which kills more than 600,000 people every year. This infographic is just a reminder of how important it is to improve health around the world. Better health conditions could make millions of people live longer and better lives. But will these better health conditions (and a longer life expectancy) actually cause economic growth? Cross-country regression studies show a strong correlation between measures of health and both the level of economic development and recent economic growth. But, as we know, correlation does not imply causation.

What Acemoglu and Johnson (AJ hereafter) do in their 2014 paper (NEP-HIS 2014-05-17) is just to play the Game of Science. AJ (2007) argue that life expectancy does not cause economic growth and that previous studies had not established a causal effect of health and disease environments on economic growth. Since countries suffering from short life expectancy are also disadvantaged in other ways that are correlated with their poor health outcomes, previous macro studies may be capturing the negative effects of these other unobservable disadvantages. To address this identification problem, AJ (2007) used an instrument for the life expectancy: medical advances that occur at the health frontier, interacted with variation in the prevalence of diseases across the world, used together to construct a predicted mortality variable. The adoption of new medical practices is clearly endogenous, but the authors argue that the technology at the frontier is potentially exogenous. Since there was variation across countries in the prevalence of different diseases, the timing of new medicine advances has a different effect on the predicted mortality for different countries. In other words, the predicted mortality variable satisfies the requirements of a good instrument: it is correlated with the life expectancy in the country, but it is arguably not correlated with other unobservables that determine growth that may be changing at the same time in a country.

Dr. Jonas Salk and Dr. Albert Sabin developed two different polio vaccines that have pretty much almost eradicated polio from the world.

Bloom et al. (2013, hereafter BCF) disagree with AJ’s strategy and conclusions. In their paper, which earlier appeared as an NBER working paper, they argue that the problem with AJ’s instrument is that it assumes the predicted mortality to be exogenous and not affected by contemporaneous income shocks. In other words, it implies that the initial mortality rate in 1940 should be unaffected by income levels in 1940, which is difficult to believe. As BCF explain very clearly, the “natural experiment” constructed by AJ is flawed. The “treatment group” that received large health gains from technological innovations is fundamentally different from the “control group” that received low health gains, since the “treatment group” had lower life expectancy initially. Therefore, if initial conditions are important for subsequent economic growth, the results will be biased if these initial conditions in 1940 are not considered. BCF included the level of life expectancy in their econometric specifications (a “partial adjustment model”) and they concluded that exogenous improvements in health due to technical advances associated with the epidemiological transition appear to have increased income levels.

In their reply to the reply, Acemoglu and Johnson (2014) address by different means the concern raised by BCF about their original work. First, in order to capture the long-run effects of the initial life expectancy, they include the level of life expectancy in 1900 interacted with time dummies in their decadal panel data set (which runs from 1940). Second, they estimate the “partial adjustment model” of BCF via non linear GMM, since the linear estimation of BCF’s specification will lead to a great deal of multicollinearity and the standard errors become very large. Finally, they use microeconomic estimates from another paper to calculate potential macroeconomic effects of current life expectancy on future growth and examine the implications of their baseline results. AJ conclude that all these approaches confirm that their main results are robust. There is no evidence that increases in life expectancy after 1940 had a positive effect on GDP per capita growth.

There are three issues in this Game of Science that I would like to comment on. First, the intent to quantify the contribution of health to economic growth is extremely relevant for both scientific and policy-related motivations. The general conclusion of the debate, at this stage of the game, is that health conditions were not a factor that shaped the differences in GDP per capita during the second half of the 20th century. Even more generally, the evidence casts doubts on the views that health has a first-order impact on economic growth. With this in mind, it is important to recognize the limitations in the study, especially to extract conclusions for today’s effect of health on economic growth. This is recognized by AJ, who warn that international epidemiological transition was a one-time event and that the diseases that take many lives in the poorer parts of the world today are not the same as those 60 years ago. Despite these considerations, it is important to notice that no author in this debate has questioned the crucial role of improving health conditions to save and improve the lives of millions of people.

Correlation versus Causation

Second, it is important to highlight that the main contribution of AJ is that they provide a sound way to address the problem of endogeneity in order to answer this important question. It is not the first time that Acemoglu and Johnson find a way to design a natural experiment to address some fundamental development questions by using exogenous variation in a country-level panel data setting. In another famous paper, Acemoglu, Johnson and Robinson (2001, AJR hereafter) address the problem of endogeneity that raises in the study of the linkages between income and institutions with the famous instrument of mortality rates of European settlers in different colonies. In both occasions Acemoglu and co-author(s) show us in practice the nuts and bolts of economists’ empirical work, that is, to address the endogeneity concerns by doing good research designs and by finding exogenous sources of variation.

Finally, I see this debate as a privileged example of Popper´s quote. In this short reply to BCF, AJ (2014) present further tests for their results in AJ (2007), overcoming the important point that BCF raise. This is a fair game; both articles are forthcoming in the Journal of Political Economy and the database and programs for AJ papers can be downloaded from Daron Acemoglu’s webpage at MIT. Even more, this is not the first time these authors play the game in the same way. A similar, and also very illustrative debate about AJR (2001) and David Albouy’s critiques can be found in the American Economic Review, or in the NBER working paper. In both debates, Acemoglu and co-author(s) present more evidence on their results that are robust to additional tests, but in both episodes we gain from the debate. We just need to recall that our knowledge is always limited by the evidence we have at the moment, and that this evidence will change over time. After all, in the Game of Science, just like in another famous game, you do not know how it is going to end, even if you read all the books that have been published on the topic.

Abstract: This paper presents a long-run analysis of industrial growth and structural change in Brazil, from the coffee export economy in the nineteenth century to the present day. We focus on Brazil’s high economic growth in most of the twentieth century and the disruption caused by the collapse of debt-led growth in the early 1980s. We then examine the recent trends in economic growth and structural change, with a sectoral analysis of output, employment and productivity growth. Employing new data and estimates, we identify a sharp break with the earlier period of high output and productivity growth in Brazil’s manufacturing industry before the 1980s. From the 1990s, the relatively successful process of learning and technological advance by manufacturing firms that took place since the early industrialization has lost strength and Brazil’s productivity growth has declined and stagnated.

They are playing soccer here.
There is much samba, much crying and rock’n’roll.
Some days it rains, on others, it shines.
But the thing I want to tell you is that things are really bad*

Chico Buarque, Brazilian musician, 1976

In four months time Brazil will be in everyone’s mind. Love it or hate it, coming June the FIFA World Cup 2014 will be in full swing and held in South America for the second time. According to Goldman Sachs, host nations can typically expect a 54pc increase in medals at the Olympic games. Assuming the relationship holds for football, this further increases the odd for the home team, which more often than not is marked as favourite by pundits across the globe, to win later this year in its home turf. Indeed, we are already hearing about Brazil because of the anti-World Cup protests. Protest which are more likely driven by unfulfilled economic expectations of Brazilians than by their rejection of the tournament.

Brazil occupies the biggest landmass in South America and has often been thought of as a big economic promise. For instance, large GDP growth rates in the late 1960s and early 1970s led people to talk about the “Brazilian miracle”. More recently, in 2009, Brazil was again a sound bite for big economic promise and the financial press coined the term “BRICs” to denominate it plus Russia, India and China as the “bright stars” in an otherwise gloomy world that was facing recession following the financial crisis. Such expectations, both in the past and today, have been fuelled by the idea of Brazil achieving a higher rate of development than others on the back of a big and highly productive manufacturing sector and long standing (and dynamic) agriculture. But Brazil has consistently failed to deliver on expectations. Even more, there is already talk of the “BIITS” to referrer to Brazil, India, Indonesia, Turkey and South Africa, while focusing on their current-account deficits and structural weaknesses (as exposed by the cooling of demand from China and the potential of the Federal Reserve hiking interest rates in the USA). But just as the Brazilian manufacturing industry has fuelled expectations, it has also been a large part of the reason behind these apparent failures.

Dante Aldrighi and Renato Colistete in this paper, circulated by NEP-HIS on 2013-08-31, offer a very detailed long-run description of industrial growth and structural change in Brazil: from the coffee export economy in the nineteenth century to the present day. They examine the recent trends in economic growth and structural change, with a sectoral analysis of output, employment and productivity growth. Their estimates show that the expansion and diversification of Brazil’s manufacturing industry from the nineteenth century until the late 1970s was a remarkable process. Despite distortions and inefficiencies, the experience of accelerated industrialization provided the country with a diversified and relatively complex industrial structure. In the 1980s and 1990s, the debt crisis and the ensuing macroeconomic imbalances undermined the manufacturing industry’s performance, weakening the incentives to invest and to improve technological capabilities.

A point of particular importance in the paper by Aldrighi and Colistete is the study of productivity. The authors show that in the last two decades the productivity growth of Brazil’s manufacturing industry has been much lower that that achieved during the earlier period of accelerated industrialization. Moreover, using a shift-share analysis they suggest that before the 1980s productivity gains within industries were a stronger driving force for aggregate productivity growth than shifts of labor to higher productivity activities. However, since the 1980s the role of structural change has become relatively more important to explain productivity growth in Brazil’s manufacture. For the economy as a whole, structural change also revealed to be more important than sectoral productivity growth in the 1990s and 2000s. They conclude that there is evidence that the relatively successful process of learning and technological advance by manufacturing firms that took place since the early industrialization has lost strength as a major source of economic growth in Brazil during the recent decades. Most of productivity growth has now been coming from agricultural activities. They also show that, during most of the period of accelerated industrialization, industrial workers saw their wages, measured in local currency, lagging consistently behind labor productivity, which led to a declining share of wages in the total income of the manufacturing sector. Later, the unit labor costs adjusted by the exchange rate increased, mainly as a result of currency appreciation and lower productivity growth. However, the authors show that labor compensation growth was modest in real terms and had a minor role in increasing unit labor costs.

FIFA World Cup 2014

The paper concludes that the main sources of concern about the performance of the manufacturing sector in Brazil rests in its very low productivity growth and the tendency to currency appreciation, which together affect unit labor costs and competitiveness. They understand that the competitiveness of manufacture might be significantly higher if the costs of inputs and services other than labor (such as capital, taxes, infrastructure, bureaucracy and innovation) were lower or declining. However, they are not optimistic about the prospect of this happening. Some of the factors that they understand have conspired to reduce efficiency and productivity growth are the complex and burdensome tax system that tends to push firms to the informal, low-productivity sector; high and unstable real interest rates; a relatively low-skilled workforce; and expenditures on R&D below the levels attained in the most dynamic developing countries, which limits the technological spillovers that might benefit the whole economy. They also state that innovation activities have been negatively affected by uncertainty and the inability to make long-horizon investment plans, increased by low and volatile public investments and economic growth rates. All these factors explain why Brazil’s investment rates remain much lower than those prevailing in most developing countries. As a consequence, the authors think that it is unlikely that Brazil’s manufacturing sector’s low productivity growth is being offset by appropriate incentives or reductions in the costs of key components that affect competitiveness in the long run.

In my opinion, the authors’ description and conclusions clearly point out the need to go beyond description and embrace new lines of research that address the specific causes of the low productivity in Brazil. These new venues of research will lead to a better understanding of the Brazilian situation and will provide a better understanding of the policy instruments that could enhance Brazil’s development. This agenda would be very beneficial for other countries in Latin and South America too, which face similar problems. Focusing on the behavior of the productivity and from a microeconomic perspective.

I would like to very briefly mention here two recent lines of research that may shed light on the causes of low productivity. The first line is related to the productivity via labor supply. Productivity seems to be affected by the poor performance of Latin American students at school. In a recent paper, Hanushek and Woessmann (2012) find that in growth regressions, the positive growth effect of educational achievement fully accounts for the poor growth performance of Latin American countries. In addition, they find through a development accounting analysis that, once educational achievement is included, human capital can account for between half and two thirds of the income differences between Latin America and the rest of the world. More efforts than those already in place (see among others Carvalho Filho and Colistete, 2010; Colistete, 2013) are necessary to better understand the links between the development of education in the region and its impact on productivity in Brazil and the region as a whole.

The second line I would like to mention is related with the productivity of firms in Brazil (and Latin America), especially managerial abilities and their impact on productivity. Managerial abilities were for long time considered in the residual of the productivity or production function equations and no consistent efforts to measure managerial abilities had been carried out. Recently, Nicholas Bloom and John Van Reenen with different coauthors have been working on surveys, based on interviews to firms, to determine management practices scores**. They have conducted interviews to more than 10,000 firms in 20 countries in the period 2004-2010. They have used this data to publish several papers on the issue that are worth looking at. Their general conclusions are that management practices scores in manufacturing vary significantly across countries and are strongly linked to the level of development. In particular, the average management practices score appears in the place 18th in the ranking only above China and India and below countries like Mexico, Chile, Argentina and Greece. The methodology the authors use for these surveys is not easy to replicate for other periods. However, this type of study provides a good insight to causes of low productivity that are often forgotten in Latin American countries and in our historical explanations and that, when measured, show our relative backwardness.

To sum up, Colistete and Aldrighi do a great job describing the evolution of the manufacturing industry in Brazil in the long run. They show how, even with very important problems, Brazil’s period of import substitution generated increases in productivity and structural change. They also document the problems that Brazil has had since the early 1980s in terms of growth and productivity. Fortunately, in all aspects besides football (ie soccer in the US), samba and rock and roll, the Brazil we have now is not the Brazil that Chico Buarque described in 1976. Among other examples, income inequality in a country that has one of the worst income distributions in the world has been improving consistently during the last few years. However, the challenges of productivity remain. Focusing in understanding the causal relationships between microeconomic factors (e.g. education achievement or managerial abilities) and productivity could help to a better understand the historical evolution of economic development and to design better policies oriented to overcome these problems.

AbstractThis paper attempts to provide a systematic analysis on the effects of industrial policy in postwar Japan. Among the various types of Japanese industrial policy, this paper focuses on the removal of de facto import quotas through the foreign exchange allocation system. Analyzing a panel of 100 Japanese manufacturing industries in the 1960s, we find that the effects of the quota removal on productivity were limited—the effects were significantly positive, but time was required before they appeared. On the other hand, the effects of tariffs on labor productivity were negative although insignificant. One possible reason for this is that the Japanese government increased tariff rates before removing the import quotas and maintained high tariff rates afterward. As a result, the effects of the Japanese industrial policy in the 1960s might be smaller than widely believed in the Japanese economic history literature.

“I haven’t got anything against open competition. If they can build a better car and sell it for less money, let ’em do it. But what burns me up is that I can’t go into Japan. We can’t build, we can’t sell, we can’t service, we can’t do a damn thing over there … I think this country ought to have the guts to stand up to unfair competition” Henry Ford II (1969)

People used to say that a miracle happened in Japan during the sixties. By 1960, the Gross Domestic Product per capita (GDPpc) of the US was 2.8 times that of Japan. In the same year, the GDP per capita of Chile was the same of the Japanese while Argentinian was 40% higher. One decade later the situation had dramatically changed. By 1970, US GDPpc was only 1.5 times greater than the Japanese. In addition, Japan GDP pc was 85% higher than the Chilean and 33% higher compared to the Argentinian. While comparison of GDPpc actually raise more questions than answers, the comparison with these Latin-American countries can be appealing because Japan and these countries had very aggressive currency controls and industrial policies during this period. The difference of results makes us think that Japan must have done something different, something better. To find these differences it is needed to evaluate separately the effects of each of the policies applied during those times, understanding the incentives that they provided to the firms. As Lars Peter Hansen – recent Nobel Prize in Economics- suggested, one key important thing in Economics is that we can do something without doing everything.

This paper, circulated in NEP-HIS 2013-11-09, focuses on the removal of de facto import quotas through the foreign exchange allocation system during the sixties in Japan. This system was used as a powerful tool for industrial policy in the 1950s, and hence their removal was supposed to have a substantial impact on industries. After direct control of international trade by the government ceased in 1949 as a part of the “Dodge Plan,” the Japanese government regulated trade indirectly through the allocation of foreign exchange. This implies that, given the prices, there was a de facto import quota for some goods, since the upper limit of the import quantity was determined by the foreign exchange budget. Under continuing pressure from the IMF, the Japanese government swiftly removed the de facto import quotas. However, this process was different from what the literature in economics refers to as trade liberalization. The removal of import quotas did not necessarily constitute trade liberalization because tariff protection was substituted for import quotas. Therefore, to correctly quantify the effects of the quota removal, it is needed to control for the effects of the tariff protection.

In order to estimate the effect of quota removal, this paper utilizes detailed industry-level data from the Census of Manufactures (100 Japanese manufacturing industries in the 1960s) and data on trade protection. This enables them to control for industry (not firm) heterogeneity while covering the majority of manufacturing industries. Based on governmental information, the authors precisely identify the timing of the quota removal for each commodity, using original documents of the Ministry of International Trade and Industry (MITI). The authors estimate the parameters of interest (effect of the quota removal and the tariffs) using least square estimation including industry and time fixed effects. In this sense, the identification strategy of the effect of the quota removal is based on the variation in the timing of the quota removal across industries.

The authors find that the effects of the quota removal on productivity were limited. None of the industry performances are systematically related to the removal of the import quotas. Additionally, an increase in tariffs generally has significantly negative effects on the number of firms, output per establishment, and industry value added. The concern about reverse causality (higher tariffs were imposed on small industries in terms of the number of establishments and value added) is addressed using leads of the tariff and quota variables. The authors also check the effects on the growth rate of the result variables, finding that the quota removal had significantly positive effects, but time was required before they appeared. One explanation they provide for this is that the Japanese government increased tariff rates before removing the import quotas and maintained high tariff rates afterward.

I think that the main takeaway from the paper is that it suggests that the effects of the Japanese industrial policy in the 1960s might be smaller than widely believed in the Japanese economic history literature. However, I think the paper will benefit if the authors discuss more clearly some aspects. First, it is important to clarify what are the intended effects of the policy and what are the mechanisms for the effect of the quota removal on productivity. A clear discussion about mechanisms and intended effects could help the reader to understand the evaluation of the policy and what are the expected results. For example, is it a good or a bad result to see increases in productivity along with a decrease in the number of establishments? It seems natural to think that the government could impose de facto quotas to limit external competition and provide a handicap for the firms during the learning process. However, it is not clear what the intention of the government was when they removed the quota. Sometimes, the quota removal could be the result of the government thinking that some firms of the industries already have an appropriate level of productivity and that the less productive firms need to exit to allocate the resource to more productive production. But sometimes, the quota removal compensated with an increase in tariffs could be just a way to update the protectionism against the lobby of the new world financial institutions.

Second, I think the paper would benefit from a more detailed discussion about the identification strategy used and its suitability. A relevant challenge to the identification is the potential endogeneity of the timing of the quota removal. Since the Outline of the Plan for Trade and Foreign Exchange Liberalization was announced before the actual liberalization took place, the firms should have had incentives and time to adjust their behavior. Additionally, as mentioned above the criteria of the government could have been based on the observed trends of the industries. Suppose that the government decided to increase more the tariffs in those sectors that already have the lowest increases in productivity and that they suppose would be the most affected from the quota removal. Since the authors do not control for the pre-existing trends of the productivity of the industries, this issue can undermine the identification strategy, which is based on the idea that the timing of the quota removal varied exogenously across industries. Controlling for time trends per industry could help to capture these potential trends, and help to control for at least this potential source of endogeneity.

Finally, a third issue is related to the identification of the coefficients for tariffs and quota removal. Even assuming that the timing of the quota removal was exogenous, an issue raises from the fact that while the tariff rate is a continuous variable the quota removal is a binary variable. However, this quota removal binary variable tries to represent a treatment effect that is potentially different by industry. In this sense, the dummy variable is only a proxy for the actual severity of the removed protection. At the same time, as it was discussed before, the loss of protection via quota removal could be correlated with the tariff increases since the authorities would have tried to compensate the affected industries. If this is the case, the tariff effect is not precisely identified since it can be capturing the unobserved heterogeneity on the severity of removed protection. In this sense, maybe the use of a continuous variable that represents the magnitude of the removed protection via the quota removal could help to better identify the effects of those variables separately.

To sum up, I think this and other papers from the same authors are making important contributions to better understand the effects of the industrial policy during postwar Japan. In this paper the authors point out that the effects of quota removal might be smaller than widely believed in the Japanese economic history literature. Even more, they point out that the effects of different policies generally overlap and that any assessment of these effects needs to take care of this fact. I cannot stress enough how important industrial policy was for postwar Japan, but if you still have doubts, you should have asked Henry Ford II.

Abstract This paper examines Gibrat’s law in England and Wales between 1801 and 1911 using a unique data set covering the entire settlement size distribution. We find that Gibrat’s law broadly holds even in the face of population doubling every fifty years, an industrial and transport revolution, and the absence of zoning laws to constrain growth. The result is strongest for the later period, and in counties most affected by the industrial revolution. The exception were villages in areas bypassed by the industrial revolution. We argue that agglomeration externalities balanced urban disamenities such as commuting costs and poor living conditions to ensure steady growth of many places, rather than exceptional growth of few.

On August 24th a couple of philosophers of science asked themselves: ¨What is Economics good for?¨ and they provided a provocative answer in the Opinionator blog of the New York Times. The main argument of the philosophers was that:

¨..the fact that the discipline of Economics hasn’t helped us improve our predictive abilities suggests it is still far from being a science, and may never be.¨

Although tempted, I will not enter the debate here but will discuss something related. Suppose you are asked to predict the population of English cities 100 years from now. Do you think London, Birmingham, Manchester and Liverpool will still be the most populated ones? Will the medium size cities of today grow as much as the smaller cities? I suppose that you will be inclined to think that the size that the city already has will be correlated with its future growth and then it is useful for prediction. Why? I imagine that you could think that the causes that have generated the growth of the cities for the last 50 or 100 years will be in one way or the other pushing their growth in the next 100 years. Alexander Klein and Tim Leunig show that this intuition is not correct, at least for England and Wales during the nineteenth and the first decade of the twentieth century.

Prediction: The image in the NYT Blog

Klein and Leunig’s work is part of a growing scholarship who has given greater attention to Gilbrat’s Law: an empirical regularity that postulates that the growth rate of places is identical for places of all initial sizes. This paper, circulated in NEP-HIS 2013-08-16, tests Gibrat’s law for the period of the most important event in the recent economic history: the British Industrial Revolution.

The Industrial Revolution seems to be such an important ¨shock¨ as to change completely the process behind the growth of the cities in the past. As the authors point out, the British Industrial Revolution implies four major interrelated changes: population grew at an unprecedented rate, both total and per capita income rose, England ceased to be a largely agrarian nation in which people’s locations were tied to the land, and the nineteenth century witnessed a transport revolution covering almost every form of transport and the energy used for transportation. Even more, this period is even more relevant given that it encompasses stage without planning nor zoning rules, so that urban growth was the result of many interacting forces.

The authors find that Gibrat’s law broadly holds, although small villages in areas bypassed by the industrial revolution tended to violate it. Moreover, although the results are similar during the entire nineteenth and the beginning of the twentieth century, it is possible to find some qualitative differences. In the nineteenth century they find a more rapid growth of large towns and cities than small ones. In contrast, at the beginning of the twentieth century in the areas that had been affected by the industrial revolution there are no differences in the growth rates of places of different sizes, because counties were at that time mature. Based on the models of Eeckhout (2004) and Córdoba (2008), the paper also offers an explanation for why Gibrat’s law largely holds even during the rapidly changing environment of the British Industrial Revolution because of the balance between positive agglomeration externalities and negative externalities of high commuting costs and large disamenities of urban life.

Two methodological factors in this paper are worth noting. First, the authors have set a unique, authoritative and comprehensive data set of all cities, towns and villages in England and Wales in the periods 1801-11, 1841-51, and 1901-11. They take advantage of the fact that Great Britain undertook the first census in 1801, and has had decennial censuses ever since. A very important factor for the test is that the descriptive statistics show that the growth in population was led primarily by the growth in the size of existing places, rather than the emergence of new places. The fact that England and Wales were populated in 1801 implied that there was no frontier to open up and that, even when some new settlements were developed (for instance along railway lines), population growth took place overwhelmingly in places that already existed. Therefore, the key urban issue in the period then is to understand what caused the growth of some cities instead of what originated new cities.

The second methodological issue is that they use a non-parametric regression analysis to test Gilbrat’s Law. In a first approach, they approximate Gilbrat’s Law using the unconditional relationship between the standardized growth rate (defined as the difference between the growth rate of the place and the sample mean divided by the sample standard deviation) and the log of population size of the place at the start of the period. The non-parametric approach (generally used in this literature) is a key factor here. Since it allows for more flexibility in the functional form of the relationship between growth and initial size, the authors are capable of showing patters of the unconditional relationship between these two variables and can test whether Gilbrat’s Law holds for the whole initial size distribution. In a second approach, the authors test another characterization of Gibrat´s Law that makes the relationship between the variance of the growth rate and the initial size. This second approach is very useful since even when the initial size could not be useful to predict the growth of the cities it could be a predictor of the variance of the growth rate.

Birmingham 1732 (taken from Wikipedia)

At this point, I would like to say that I think this research line would advance significantly if the authors considered the inclusion of other factors to explore the relation between growth and size conditional on these factors. As they establish, the Industrial Revolution completely changed many factors that can be influencing the growth rates of the cities. Just as an example, imagine what could happen with two cities that are identical in all aspects except that one is close to a coal mine. It is likely that the city close to a coal mine will grow more, just because the transport costs of this new energy source are lower. Obviously these factors included in the agglomeration externalities that balance with the commuting cost or disamenities of urban life (negative externalities). But including those factors in a regression between growth and size would provide a more reliably consistent estimation of the (partial) effects of size and the other factors on city growth. In this sense, a multivariate regression of the growth rates over size and other factors would have a more clear the interpretation (and measure) of the economies (and diseconomies) of agglomeration that are suggested and discussed in this paper and that drove the growth of the cities during the period.

To sum up, I think the paper made a very important contribution robustly showing that there is no unconditional relationship between growth and initial size (besides some small villages bypassed for the Industrial Revolution). The authors provide useful explanations of why this could have happen along of the lines of the models in Eeckhourt (2004) and Cordoba (2008). In short, the authors show that the British Industrial revolution was such an important ¨shock¨ that changed completely the process of growth of the cities, changing the balances between economies and diseconomies of agglomeration. With this in hand and with the incredible database they have constructed, they have also opened the possibility of formally testing the conditional (partial) effects of size and other factors on city growth. This would help to understand the dynamics behind city growth during this key period of time. Furthermore, while the (lack of) unconditional relation between city size and growth rate leaves us ill-equipped for predicting future city growth, the identification of the conditional partial effect of size on growth could improve our “predictive abilities”.

Are Government Spending Multipliers Greater During Periods of Slack? Evidence from 20th Century Historical Data

Michael T. Owyang, Valerie A. Ramey, Sarah Zubairy

Abstract

A key question that has arisen during recent debates is whether government spending multipliers are larger during times when resources are idle. This paper seeks to shed light on this question by analyzing new quarterly historical data covering multiple large wars and depressions in the U.S. and Canada. Using an extension of Ramey’s (2011) military news series and Jordà’s (2005) method for estimating impulse responses, we find no evidence that multipliers are greater during periods of high unemployment in the U.S. In every case, the estimated multipliers are below unity. We do find some evidence of higher multipliers during periods of slack in Canada, with some multipliers above unity.

For a very long time the size of the expenditure multipliers has been one of the most vivid economic debates. For instance as recently as 2009, when the Obama administration proposed a fiscal stimulus package, there was a heated discussion regarding the relative size of the expenditure and tax multipliers. The reason fuelling this narrative is perhaps clear: ascertaining the potential impact of a particular proposed measure is key when designing the fiscal policy.

The paper by Owyang, Ramey and Zubairy, which was distributed by NEP-HIS on 2013-02-08 tries to answer this question: Are government spending multipliers greater during periods of slack for the US and Canada when we look at the historical data? The argument behind it is to consider that the expenditure multipliers will be greater in times of crisis, that is, during periods without full employment of labor and capital in the economy. This argument follows the idea that to wake up animal instincts, you need to have something in the forest when guys go out to hunt.

The answer that the authors offer is counterintuitive, which makes the paper very interesting. They find that the expenditure multipliers were higher in periods with high unemployment in Canada but they were the same for both periods in the US. To arrive to this conclusion the authors first construct high frequency (quarterly) historical data for the US and Canada. The procedure they follow to build the database is documented in an online available annex of the paper (here). After this process they have data on GPD, GDP deflator, government spending and the unemployment rate for the period 1890q1 to 2010q4 for the US and from 1921q1 to 2011q4 for Canada. The other key variable is the “news” variable, which reflects the changes in expected present value of government spending in response to military events as in Ramey (2011), which in turns directs to Ramey (2009).

Regarding the econometric approach, the authors use Jorda’s (2005) local projection technique to calculate impulse responses. The idea in Jorda (2005) is that, in contrast to VAR approaches which linearly approximate the data generating process to produce optimal one period forecasts, when we are looking at impulse response analysis we should care about the estimation of longer horizons. In this context, it is a better approach to estimate the impulse responses consistently by a sequence of projections of the endogenous variables shifted forward in time onto their lags using ordinary least squares (OLS) with standard errors addressing heterogeneity and serial correlation. The authors estimate a set of OLS regressions of different number of leads of the log of per capita government expenditure and GDP, over their lags and the variable news for periods with high and low unemployment and a quadratic trend. The coefficient for the variable “news” is the impulse response at that certain number of lags.

Finally, the paper made me think of three comments. First of all, the paper shows a very interesting and creative way to proceed when the data needed for the study is actually not available for that historical period. Besides combining sources of information, the authors constructed quarterly series of the variables. Since the paper was prepared for the American Economic Review Paper and Proceedings, it is a very short paper and the procedure to construct the variables is explained not in the paper but in the Annex. Given the lack of data, assumptions about the data generating process must be made. However, and besides the obvious limitation of space, the reader could miss an explanation about the assumptions that are made in the methods used and, also, what implications these assumptions have for the results, in particular about what is the source of variation that allows the identification of the coefficients. Maybe a section in the paper or in the appendix discussing these issues can shed light about what are the potential problems of different assumptions.

The last two comments are related to what is exactly the interpretation of the results. The first one directly follows from the last sentence of the paper. The authors state that they do not adjust for the fact that taxes often rise at the same time as government spending, which turns these multipliers not equal to pure deficit financed multipliers. However, it seems plausible that the effect of the multiplier on the GPD depends on whether this increase in the government was financed by taxes or by debt. If that is the case, and if the episodes when the former and the latter happen are mixed in a non-random way between the periods of high and low unemployment, then it is possible that the value of the coefficients can reflect not only the effect of the exogenous shock but also the effect of different ways to finance it.

A joke?

The last comment relates to the consistent estimation of the parameters of the model. In the paper the “news” about military expenditure is taken as the only source of exogenous shock in this economy during the period of two years, four years and the time of the peak of each response. This “news” variable reflects exogenous innovations to the expenditure from a military source. However, it would be relevant for the paper to discuss the existence of other (non-military) sources of exogenous shocks to the expenditure. The relevance of this issue is because, given that the estimation of the parameters of interest is done by OLS, the consistency of the estimates requires zero covariance between the ¨news” and the error term of the equation, and this assumption can be violated if there exist this kind of non-military shocks and they are correlated to military “news”.

Overall I think this is a very interesting paper because of the results they find and also because of the construction of historical data. I found the results very puzzling and therefore a big motivation to continue trying to understand the relationship between GDP and public expenditure.